code... Could
anyone point out what the problem is?
Thanks.
Wuming
Save your time (and much execution time) by using the Hmisc package's
rcorr.cens function with the argument outx=TRUE. rcorr.cens using a
standard U-statistic variance estimator.
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of C-index.
Thank you very much in advance.
Regards, Adai
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nonlinear effects, interactions, etc.
If by get you mean retrieve for use in other code you can look at
print.lrm for hints, or use coef(fit) and diag(Varcov(fit)).
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of such a stratified test as part of a proportional odds
model with adjustment for strata as main effects. The Wilcoxon tests is
a special case of the PO model. You can fit it with polr or lrm.
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method,
within paired predictions. You can't get a difference in ROC areas
from the U-statistic computed by rcorrp.cens.
Frank
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that Brian Ripley mentioned) and wanted to use that to
enhance print. and latex. methods for the summary.formula family of
functions, and know or can learn how to use CVS, we will consider giving
you access to the Hmisc CVS repository to make the enhancements you want.
Frank
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bogdan romocea wrote:
The first one is an index, not a data set. Anyway, just use SAS to
export the data sets in text format (CSV, tab-delimited etc). You can
then easily read those in R. (By the way, the help for read.xport says
that 'The file must be in SAS XPORT format.' Is .sas7bdat an
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, df)) ## What is alpha if using
T-statistic ?
return(list(w.alpha=w.alpha, alpha.star=alpha.star))
}
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-value printing as requested. Look at anova.Design for
the code. -Frank
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$P
0.001080
CT23$V
0.2643529
But I do not know how.
cheers
Worik
library(Hmisc)
rcorr(cbind(. . . .))
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with that many
variables. -Frank Harrell
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by constructing something on
the right hand side of the model. But if you have a simple historical
covariate such as previous history of disease before the start of
follow-up time that's ok.
Frank
Original Message - From: Frank E Harrell Jr To: Stephen Cc:
Sent: Monday, July 18, 2005 2:46 PM
don't require categorization. You can request predicted
values at any sequence of ages. If you want hazard ratios you can take
differences in predicted log hazards and antilog them.
Frank
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with sasxport.get that reads csv files. The help
file has a URL with a full howto.
Frank
Thanks.
-Original Message-
From: Frank E Harrell Jr [mailto:[EMAIL PROTECTED]
Sent: Thursday, July 14, 2005 11:46 AM
To: bogdan romocea
Cc: Nelson, Gary (FWE); R-help@stat.math.ethz.ch
Subject: Re: [R
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, :
inconsistant naming of observations led to differing length
vectors
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distribution.
so that the Rubin/Shafer method described above (see paragraph
about dispersion of imputed values) is not fully implemented.
Best wishes,
Ted.
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be a fairly common task with a straight
forward solution that I can't see. Any ideas?
Best wishes,
Mikkel
library(Hmisc)
?combine.levels
This doesn't remove levels but combines infrequent ones though.
Frank
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- prelim.norm(tc)
thetahat - em.norm(s) #find the MLE for a starting value
theta - da.norm(s,thetahat,steps=20,showits=TRUE,return.ymis=TRUE) #take 20
steps
ximp - imp.norm(s,thetahat,tc) #impute missing data under the MLE
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Marten Winter wrote:
Now that's a brief note. mApply in the Hmisc package may have large
speed advantages over by for large datasets. Also look at the summarize
function in Hmisc, which uses mApply.
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0.89438636
version
platform i386-pc-linux-gnu
arch i386
os linux-gnu
system i386, linux-gnu
status
major2
minor1.0
year 2005
month04
day 18
language R
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,
Federico Calboli
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Can someone point me to R code or recent publications dealing with
selection of representative time-response profiles in longitudinal data
from datasets containing a large number of subject's profiles?
Thanks
Frank
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at documentation for ols
and validate.ols.
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Prof Brian Ripley wrote:
On Tue, 14 Jun 2005, Frank E Harrell Jr wrote:
Prof Brian Ripley wrote:
On Tue, 14 Jun 2005, James Salsman wrote:
How can ordinary polynomial coefficients be calculated
from an orthogonal polynomial fit?
Why would you want to do that? predict() is perfectly
for your help.
Carsten
There are functions in the Hmisc package to help too (especially
labcurve and xYplot). You can have a function put a legend in the most
empty portion of a graph automatically with these functions.
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have not quite got this right...can you help?
Thanks in advance
Sam
Sam McClatchie,
Biological oceanography
South Australian Aquatic Sciences Centre
You might try the summarize function in the Hmisc package.
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a single model :-)
Frank Harrell
Anyhow, is there a function to produce the right hand side of the
formulas, please?
thanks,
Laura Holt
mailto: [EMAIL PROTECTED]
R Version 2.1.0 Windows
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might also look at section 4.3 of
http://biostat.mc.vanderbilt.edu/twiki/pub/Main/RS/sintro.pdf
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Uwe Ligges wrote:
Aric Gregson wrote:
On 5/17/05 21:44 Frank E Harrell Jr sent the following:
Aric Gregson wrote:
Hello,
I am trying to use the following to output a table to latex:
cohortbyagesummary - by(data.frame(age,ethnicity), cohort, summary)
w - latex.default(cohortbyagesummary
.
Frank
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problem because Hmisc has being using label and
label- since 1991.
Frank
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, and am changing - to
left arrows and ~ to $\sim$. This also requires the LaTeX relsize package.
Thanks
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coefficients?
Thanks in advance!
Alexander Roth
Design doesn't implement those because they have terrible properties.
Instead consider interquartile-range odds ratios (done by summary.Design
by typing summary(. . .)).
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(cmd)
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PLEASE do read
,
calibrate.lrm
Frank
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predictors)
x[,sample(ncol(x))]
:-) -Frank
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(Ted Harding) wrote:
On 03-May-05 Frank E Harrell Jr wrote:
walmir-rodrigues wrote:
Dear Fellows,
How can I do to proced a step wise regression in R, if it´s possible ?
Thanks,
Walmir
Here is an easy approach that will yield results only slightly less
valid than one actually using the response
don't), Design wants you to use glmD.
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, and an assumption of high
power for the tests (qq plots don't always help with that because of
their subjectivity). When possible it's good to choose a robust method.
Also, doing pre-testing for normality can affect the type I error of
the overall analysis.
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IL 61820
On Apr 28, 2005, at 7:46 AM, Frank E Harrell Jr wrote:
Usually (but not always) doing tests of normality reflect a lack of
understanding of the power of rank tests, and an assumption of high
power for the tests (qq plots don't always help with that because of
their subjectivity). When
=matrix(0,1000,1)
x[100]=1
Classify.Quantile(x, 10)
of course this dataset is a bit extreme but it happens to get data
with very small variance.
Thanks for any help you could provide
The cut2 function in the Hmisc package may help. -FH
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like exactly what aregImput() is good for, or
transcan(), depending on whether you need to make inferences (and
hence do multiple imputation).
Jon
For those interested I have preprints of a paper comparing MICE,
aregImpute, and transcan on the basis of simulations.
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272866 (PA)
Oxford OX1 3TG, UKFax: +44 1865 272595
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-chosen models or you
will invalidate inference and estimates if you use these comparisons to
build a final model.
Frank
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Tel: +32-16-329750 Fax: +32-16-329760
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. Unfortunately I
don't know how to calculate the AUC. Someone can point me an example of
this procedure or a package that implements this calculation?
Thanks for the help,
João Paulo Dubas.
One of many ways:
trap.rule - function(x,y) sum(diff(x)*(y[-1]+y[-length(y)]))/2
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.
Frank Harrell
On Tue, 5 Apr 2005, Frank E Harrell Jr wrote:
[EMAIL PROTECTED] wrote:
Dear R-list,
i have 6 different sets of samples. Each sample has about 5000 observations,
with each observation comprised of 150 baseline covariates (X), 125 of which
are dichotomous. Roughly 20
I wish to perform brain surgery this afternoon at 4pm and don't know
where to start. My background is the history of great statistician
sports legends but I am willing to learn. I know there are courses and
numerous books on brain surgery but I don't have the time for those.
Please direct me
Baarle
[EMAIL PROTECTED]
You didn't say which prostate cancer study that represented. One famous
one has data in R form on our web site http://biostat.mc.vanderbilt.edu.
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New York, NY 10010
(212) 845-4485 (voice)
(917) 438-0894 (fax)
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Trevor Wiens wrote:
On Thu, 10 Mar 2005 16:19:41 -0600
Frank E Harrell Jr [EMAIL PROTECTED] wrote:
The goodness of fit test only works on prespecified models. It is not
valid when stepwise variable selection is used (unless perhaps you use
alpha=0.5).
Perhaps I'm blind, but I can't find any
on prespecified models. It is not
valid when stepwise variable selection is used (unless perhaps you use
alpha=0.5).
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Congratulations Dirk on the article in linuxtoday.com today about Quantian.
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linear models
(particularly the fitting
of logistic relationships), the complexity is greatly increased.
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PLEASE do read
PROTECTED] On Behalf Of Frank
E Harrell Jr
Sent: Wednesday, March 02, 2005 5:13 AM
To: Wittner, Ben
Cc: [EMAIL PROTECTED]
Subject: Re: [R] subset selection for logistic regression
Wittner, Ben wrote:
R-packages leaps and subselect implement various methods of
selecting best or
good subsets
(manuals, FAQ, community) as a starter to learn, use and program R by
themselves.
I think this would do for a 40 minutes presentation without taking the
risk to deter people due to overcomplexity.
regards
Thomas
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accuracy and that violates every aspect of statistical inference, you
are on the right track. See
http://www.stata.com/support/faqs/stat/stepwise.html for details.
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.
.
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PLEASE do read
at the Bioconductor website. There
was also some
code sent around on the list a few months back for calculating trapeziodal AUC,
se's
from ROC, and comparing two ROC curves...search the archives if interested, or
I can
probably dig them out for you offline...
Cheers,
Joe
Quoting Frank E Harrell Jr [EMAIL
not need to
create new variables at all.
Franc
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get them?
Thanks
The Design package's survplot function can print n.risk over equally
spaced time points. You might see an easy way to print this by looking
at the code. -Frank
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=NA, fun='plogis') # probability scale
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library(Design)
d - datadist(mydata); options(datadist='d')
f - lrm(incidence ~ sun*trees) # lrm is for binary or ordinal response
plot(f, sun=NA, trees=NA)
# add method='image' or 'contour' to get other types of graphs
plot(f, sun=NA, trees=NA, fun='plogis') # probability scale
Correction:
do not know and have not
figured out how to do that.
Any suggestion(s) would be greatly appreciated.
Thanks,
Charles
The csv.get function in the Hmisc package may do most of what you want.
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(parametric model) ?
Thanks
Virginie
In the Design package look at the pphsm function that converts a survreg
Weibull fit (fitted by the psm function which is an adaptation of
survreg) to PH form.
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can't use lm(), because robcov() needs an object from the Design() series.
Or is there a different way to go about this?
Tobias Muhlhofer
ols does not support this. Sorry.
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the scale), you will have to modify the
panel.bwplot function in addition to using the above.
--sundar
You may also want to try
library(Hmisc)
library(lattice)
bwplot(..., panel=panel.bpplot)
?panel.bpplot
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in
Wilcoxon. As Somers' Dxy rank correlation coefficient is 2*(1-C) where
C is the concordance or ROC area, the Hmisc package function rcorr.cens
uses U statistic methods to get the standard error of Dxy. You can
easily translate this to a standard error of C.
Frank
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frame with the NA values filled up?
Or is there any other function that i could use?
Thank you
avneet
It's in the help file for transcan. But multiple imputation is much
better, and transcan does not do multiple imputation as well as the
newer Hmisc function aregImpute.
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Dan Bolser wrote:
On Wed, 12 Jan 2005, Frank E Harrell Jr wrote:
Dan Bolser wrote:
Hello,
I am making some use of ROC curve analysis.
I find much help on the mailing list, and I have used the Area Under the
Curve (AUC) functions from the ROC function in the bioconductor project...
http
to include their names in an
Ack. section, usually. Referencing as a personal communication is
perhaps better.
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Prof Brian Ripley wrote:
On Sun, 9 Jan 2005, Anne wrote:
I'm about to present a report (for internal use of governmental
agency). I used extensively R , contibuted packages, as well as
communications on the R-list
As well as citing R, I would like to know how to cite the contributed
packages
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